Detecting Emotion in Music

Abstract

Detection of emotion in music sounds is an important problem in music indexing. This paper studies the problem of identifying emotion in music by sound signal processing. The problem is cast as a multiclass classification problem, decomposed as a multiple binary classification problem, and is resolved with the use of Support Vector Machines trained on the timbral textures, rhythmic contents, and pitch contents extracted from the sound data. Experiments were carried out on a data set consisting of 499 30-second long music sounds over ambient, classical, fusion, and jazz. Classification into the ten adjective groups of Farnsworth (plus three additional groups) as well as classification into six supergroups that are formed by combining these basic groups was attempted. For some groups and supergroups reasonably accurate performance was achieved.